class_docstr: Abstract object detector dataset module.
  
  This module implements some of the basic functionalities every object
  detector dataset usually needs.
  
  Object detection datasets are datasets that have ground-truth information
  consisting of rectangular bounding boxes.
  
  Attributes:
      dataset_dir (str): Base directory of the dataset.
      num_epochs (int): Number of epochs the dataset should iterate over.
      batch_size (int): Batch size the module should return.
      split (str): Split to consume the data from (usually "train", "val" or
          "test").
      image_min_size (int): Image minimum size, used for resizing images if
          needed.
      image_max_size (int): Image maximum size.
      random_shuffle (bool): To consume the dataset using random shuffle or
          to just use a regular FIFO queue.
code: def _sparse_to_tensor(self, sparse_tensor, dtype=tf.int32, axis=[1]):
      return tf.squeeze(
          tf.cast(tf.sparse_tensor_to_dense(sparse_tensor), dtype),
          axis=axis,
      )
  
file_path: luminoth/luminoth/datasets/object_detection_dataset.py
imports: import tensorflow
  from luminoth.datasets.base_dataset import BaseDataset
  from luminoth.utils.image import resize_image_fixed, resize_image, flip_image, random_patch, random_resize, random_distortion, expand
label_desc: Use image node_id 6 for calling the ObjectDetectionDataset obj's underlying member method code with example usage: obj._sparse_to_tensor(sparse_tensor, dtype, axis) and returns: tf